The results summarize first the relationship between the selected environmental variables and between them and the species occurrence. These variables were used to fit the best model explaining the distribution of each species, with the Chara strigosa model presented in detail. Finally, a scenario of climate change applied to this model resulted in a prediction of a future occurrence distribution of the species in Switzerland.
49 3.1. Relation between variables
Correlation between the chosen environmental variables was generally low (Table 2).
July precipitation and mean July temperature were slightly correlated to each other;
when the temperature increases the precipitation tends to decrease (Pearson correlation: r = - 0.29). The correlation between the proportion of forest and the mean July temperature was slightly positive in the close surroundings of the waterbody (r = 0.22) and moderately positive within the catchment area (r = 0.40). Looking deeper at this relationship (graph not shown), the correlation was due to an absence of forest in catchment area characterized by mean July temperature below 12°C, hence to waterbodies located at higher altitudes. Without these data, the correlation dropped and there was almost no relation between both variables (r = - 0.03). Because the proportions of forest and of land cultivated are inversely proportional, the relationship between them was negative. The proportions of forest in the surroundings and in the catchment areas were only moderately correlated to each other (r=0.65), indicating that these two variables have a different potential as explanatory variables. The same kind of relationship was measured between proportions of land cultivated at the two spatial scales (r = 0.58) (Fig. 1).
3.2. Species distribution along the selected preditors
Records of C. strigosa, C. tomentosa, Nitellopsis obtusa and Tolypella glomerata were almost all restricted to large waterbodies (>10 ha), corresponding to lake shore segments (Fig. 2), while species like Chara intermedia and Chara vulgaris were found mainly in smaller ecosystems (less than 1000 m2).
Regarding the proportion of land used by agriculture in the catchment area, the distribution did not differ significantly between species (p-value = 0.45) but did vary much more according to the proportion of agriculture in the close surroundings. For example, seventy-five percent of the ecosystems harboring N. obtusa and T. glomerata had less than 30 % in land cultivated in the neighboring area because land around lowland lakes is mainly urbanized. The proportion of land used by agriculture approached 60 % in the 200 m belt around the majority of waterbodies harboring C.
vulgaris and Nitella syncarpa.
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Three quarters of the localities harboring N. obtusa had less than 30 % of their catchment area covered by forest, while those hosting C. intermedia had the highest proportion, ranging from 30 to 70 %. In a similar way, this species also grew in waterbodies with the highest proportion of forest in the surroundings (200 m) like C.
strigosa and N. opaca. By contrast, 75 % of C. tomentosa and T. glomerata data were recorded in waterbodies supporting the lowest proportion of forest in the surroundings, conditions which correspond to the larger ecosystems, i.e. the shores of lakes.
C. vulgaris was found in ecosystems having a mean July temperature lower than 5 °C as well as in those having about 20 °C. In fact, C. vulgaris is the species that has been found at the higher altitude range. C. globularis was recorded along the whole gradient of mean July temperatures, although 75% of the data were restricted around 18°C. C.
strigosa colonized the colder ecosystems with generally a very restricted mean July air temperature gradient. N. opaca also colonized colder ecosystems of our dataset, with few records over 15°C. All species were found in localities for which mean July temperature did not exceed 20°C except C. contraria, C. vulgaris and C. globularis which were also found at warmer sites. C. tomentosa, N. obtusa, and T. glomerata distribution was almost limited to ecosystems having a 17-18°C mean July temperature.
N. obtusa was recorded in regions receiving lower rainfall, unlike C. tomentosa, T.
glomerata and N.syncarpa that were found in higher rainfall areas.
T. glomerata was recorded in waterbodies located in CaCO3-rich catchment areas, whereas N. obtusa and N. opaca were more commonly in low carbonate catchments.
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Fig. 2: Species distribution along environmental predictor used to build the models.
Legend: log(area) = the size (in m2) of the locality (a pond or a lake shore segment), AGRIBV = proportion of land used by agriculture within the drainage basin, AGR200 = proportion (%) of land used by agriculture in the surroundings (200m) ; FORETBV = proportion of land covered by forest in the drainage basin, FO200 = proportion of land covered by forest in the surroundings (200m), Tju = mean July temperature (°C x 100) , Preju = July precipitation (mm), CaCO3BV= median of calcium carbonate content in the parent material in the drainage basin. See Table 1 for species abbreviation.
The p-value from Kruskal-Wallis tests are shown on each plot. The horizontal line in the middle of the black box represents the median (50% of the values) and those closing the box correspond to the lower quartile (25 %) and the higher quartile (75 %). Each whisker extends to the most extreme data point whose value is not more than 1.5 times the interquartile range for the box. Values outside this range are drawn as dots.
52 3.3. Species Distribution Models
Chara virgata, Chara filiformis, Chara polyacantha, Chara tenuispina, Nitella flexilis, Nitella gracilis, Nitella mucronata and Nitella syncarpa could not be modeled. They are the most endangered species, whose occurrence was low (less than 11 records). The other twelve species recorded were modeled successfully (Table 4).
Table 4: Species distribution model (GAM) of 12 charophyte species. The complete names of the species are given in Table 1. Variables selected: waterbody area (log transformed), soil parent material in the drainage basin (CaBV5c), July mean temperature (Tju), July precipitation (Preju), land-use variables: proportion of forest and agriculture in a 200 m buffer around the waterbody (FO200, AGR200); proportion of forest and agriculture in the drainage basin (FORESTBV, AGRIBV). Model evaluation: Receiver operating characteristic (ROC), cross-validated ROC (cvROC), explained deviance (D2). The most contributory variable in each model is highlighted in bold, the second in italic.
predictors contribution in the models model evaluation
species
s(log(area)), 3) s(Tju, 3) s(FORETBV, 3) s(Preju, 3) s(CaCO3_BV, 3) s(FO200, 3) s(AGR200, 3) s(AGRIBV, 3) Nb predictors included in model ROC cvROC D2 Species occurrence observed in 1402 localities Species occurrence predicted in 21'092 localities
chasp 5.0 3.0 1.2 3.6 0.6 0.7 6 0.87 0.83 0.26 75 431 reasonably good (ROC =0.7-0.9) to very good (0.9-1.0). This was also true for the ROC values obtained by validation (cvROC). A ROC value, staying high after cross-validation (cvROC), indicates a model with high stability. Six of our models explained a relatively high proportion of total deviance (D2 = 30 to 76 %, Table 4) which indicates
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the relevance of the variables used. Taking stability and explained deviance into account as well as the visual evaluation, the best models were obtained for N. obtusa, C. strigosa and C. tomentosa. A second group of reasonably good models with moderate stability or explained deviance included C. contraria and N.opaca. The variables tested in this paper account poorly for the distribution of C. vulgaris, as shown by the very low deviance
The contributions of the predictors selected in each of the eleven models are shown in Table 4. All predictors were included at least twice in the models. The waterbody size (log(area)) was included in all 11 models and was the most significant factor explaining charophyte distribution for seven species. The second most important predictor was the mean July temperature (Tju), retained in 10 models. Temperature was the strongest contributing factor explaining the distribution of C. virgata and C. intermedia and the second most important for C. globularis, N. obtusa and T. glomerata.
The proportion of land covered by forest in the catchment area (FORETBV) and July precipitation (Preju) were important to the majority of our species models (8 and 7 models respectively). The calcareous content of the soil in the catchment area also played an important role. The proportion of soil cover by forest in the surrounding of waterbodies (FO200) and agriculture (AGR200, AGRIBV) were found to contribute less to the variation of species distribution.
3.4. The distribution of C. strigosa
The response curves of C. strigosa are presented as an illustration of species answer to environmental predictors (Fig. 3). Four predictors were retained in the model and explained 67 % of the total deviance: waterbody size, mean July temperature, proportion of forest in the catchment area and July precipitation (Table 4).
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Waterbody size (log(area)) was the strongest contributing variable in the species model (contribution 22.0 in the range of the predictor scale) closely followed by July precipitation (contribution 19.9), then by mean July temperature (contribution 7.3) and the proportion of forest covering the catchment area (contribution 2.8). According to the model, C. strigosa distribution increased with the waterbody size, reflecting the higher probability of finding this species in lakes than in small ecosystems (Fig. 3). Similarly, the probability of finding the species was predicted to decrease with increasing precipitation, with a marked decrease for localities exhibiting sum of rainfalls higher than 1400 mm in July. The species distribution showed a bell shape to mean July temperature, indicating higher occurrence in a narrow range of cool mean July temperatures. Likewise, the species distribution showed a bell-shape with increasing proportion of forest covering the catchment area. Finally, the model indicated that localities having the maximum probability of sheltering C. strigosa, combines a waterbody size of about 10 ha, July precipitations lower than 1100 mm, a mean July temperature around 12 °C (data 1960-1990) and a catchment area covered with 50 % of forest. During our fieldwork (1402 localities examined) C. strigosa has been observed in 21 localities (ponds and 1km lake shores). Based on the present model, the species is predicted to occur currently in 50 localities in the country (of 21,092 potential candidates). The response plots of the other species modeled are presented in Supplementary material.
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Fig. 3: Chara strigosa GAM model.
The size (log area) contributes most in the model, followed by the sum of July precipitation and then the mean July temperature and proportion of forest within the drainage basin. The weight of each predictor is reported in Table 4. The solid line indicates the smoothing curve and the dotted line the confidence intervals including the uncertainty about overall mean.
3.5. Species distribution under scenarios of climate change
The models used as a prediction tool under a climate scenario yielded the potential future occurrence of the species. A temperature increase (2°C) and precipitation decrease (15%) will have an effect on the occurrence of most charophyte species (Fig.
4). Half of them will most likely increase their distribution while the other half is likely to decrease in Switzerland.
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Fig. 4 Charophyte species occurrence change (%) as predicted by our scenario climate change. The Kolmogorov-Smirnov significances on each plot were all with a p-values < 0. 001. The complete names of species and categories of threat are given in Table 1.
Our climate change scenario anticipated the occurrence of C. hispida to rise by more than 90 %. To a lesser extent C. globularis (15%) and C. aspera (14%) will also become more common, while C. strigosa and C. intermedia are likely to show a slight increase (less than 10 % increase). By contrast, the decrease in occurrence of C. contraria (49 %), C. virgata (41%) and C. tomentosa (25 %) will be quite significant. Besides considerable expansions and contractions, our climate change scenario showed little decrease in the occurrence of N. opaca (15 %) and N. obtusa (5%).
The p-value of Kolmogorov-Smirnov test comparing the probabilities of occurrence before and after applying the scenario were significantly different for all the species.
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